


import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 	__lowercase					(	snake_case__			,       unittest.TestCase	):
      UpperCamelCase										=							KandinskyVaaPriorPipeline
      UpperCamelCase										=							['''prompt''']
      UpperCamelCase										=							['''prompt''', '''negative_prompt''']
      UpperCamelCase										=							[
          '''num_images_per_prompt''',
          '''generator''',
          '''num_inference_steps''',
          '''latents''',
          '''negative_prompt''',
          '''guidance_scale''',
          '''output_type''',
          '''return_dict''',
      ]
      UpperCamelCase										=							False





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> str:






         """simple docstring"""







         return 3_2





      @property
      def 							_lowercase					(		self    :       str							)     -> int:






         """simple docstring"""







         return 3_2





      @property
      def 							_lowercase					(		self    :       Any							)     -> List[Any]:






         """simple docstring"""







         return self.time_input_dim





      @property
      def 							_lowercase					(		self    :       int							)     -> str:






         """simple docstring"""







         return self.time_input_dim * 4





      @property
      def 							_lowercase					(		self    :       Any							)     -> Dict:






         """simple docstring"""







         return 1_0_0





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
         return tokenizer





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> int:






         """simple docstring"""







         torch.manual_seed(0							)
         UpperCAmelCase      					=  CLIPTextConfig(
             bos_token_id=0       ,							eos_token_id=2       ,							hidden_size=self.text_embedder_hidden_size       ,							projection_dim=self.text_embedder_hidden_size       ,							intermediate_size=3_7       ,							layer_norm_eps=1e-0_5       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=1_0_0_0       ,							)
         return CLIPTextModelWithProjection(_UpperCAmelCase							)





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> List[str]:






         """simple docstring"""







         torch.manual_seed(0							)

         UpperCAmelCase      					=  {
             """num_attention_heads""": 2,
             """attention_head_dim""": 1_2,
             """embedding_dim""": self.text_embedder_hidden_size,
             """num_layers""": 1,
         }

         UpperCAmelCase      					=  PriorTransformer(**_UpperCAmelCase							)
         # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
         UpperCAmelCase      					=  nn.Parameter(torch.ones(model.clip_std.shape							)							)
         return model





      @property
      def 							_lowercase					(		self    :       int							)     -> List[str]:






         """simple docstring"""







         torch.manual_seed(0							)
         UpperCAmelCase      					=  CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size       ,							image_size=2_2_4       ,							projection_dim=self.text_embedder_hidden_size       ,							intermediate_size=3_7       ,							num_attention_heads=4       ,							num_channels=3       ,							num_hidden_layers=5       ,							patch_size=1_4       ,							)

         UpperCAmelCase      					=  CLIPVisionModelWithProjection(_UpperCAmelCase							)
         return model





      @property
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  CLIPImageProcessor(
             crop_size=2_2_4       ,							do_center_crop=_UpperCAmelCase       ,							do_normalize=_UpperCAmelCase       ,							do_resize=_UpperCAmelCase       ,							image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]       ,							image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]       ,							resample=3       ,							size=2_2_4       ,							)

         return image_processor





      def 							_lowercase					(		self    :       str							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  self.dummy_prior
         UpperCAmelCase      					=  self.dummy_image_encoder
         UpperCAmelCase      					=  self.dummy_text_encoder
         UpperCAmelCase      					=  self.dummy_tokenizer
         UpperCAmelCase      					=  self.dummy_image_processor

         UpperCAmelCase      					=  UnCLIPScheduler(
             variance_type="""fixed_small_log"""       ,							prediction_type="""sample"""       ,							num_train_timesteps=1_0_0_0       ,							clip_sample=_UpperCAmelCase       ,							clip_sample_range=10.0       ,							)

         UpperCAmelCase      					=  {
             """prior""": prior,
             """image_encoder""": image_encoder,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """scheduler""": scheduler,
             """image_processor""": image_processor,
         }

         return components





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       List[Any]       ,							__lowerCamelCase    :       str=0							)     -> List[str]:






         """simple docstring"""







         if str(_UpperCAmelCase							).startswith("""mps"""							):
            UpperCAmelCase      					=  torch.manual_seed(_UpperCAmelCase							)
         else:
            UpperCAmelCase      					=  torch.Generator(device=_UpperCAmelCase							).manual_seed(_UpperCAmelCase							)
         UpperCAmelCase      					=  {
             """prompt""": """horse""",
             """generator""": generator,
             """guidance_scale""": 4.0,
             """num_inference_steps""": 2,
             """output_type""": """np""",
         }
         return inputs





      def 							_lowercase					(		self    :       Optional[int]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  """cpu"""

         UpperCAmelCase      					=  self.get_dummy_components()

         UpperCAmelCase      					=  self.pipeline_class(**_UpperCAmelCase							)
         UpperCAmelCase      					=  pipe.to(_UpperCAmelCase							)

         pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

         UpperCAmelCase      					=  pipe(**self.get_dummy_inputs(_UpperCAmelCase							)							)
         UpperCAmelCase      					=  output.image_embeds

         UpperCAmelCase      					=  pipe(
             **self.get_dummy_inputs(_UpperCAmelCase							)       ,							return_dict=_UpperCAmelCase       ,							)[0]

         UpperCAmelCase      					=  image[0, -1_0:]
         UpperCAmelCase      					=  image_from_tuple[0, -1_0:]

         assert image.shape == (1, 3_2)

         UpperCAmelCase      					=  np.array(
             [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2





      @skip_mps
      def 							_lowercase					(		self    :       Optional[int]							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  torch_device == """cpu"""
         UpperCAmelCase      					=  True
         UpperCAmelCase      					=  False

         self._test_inference_batch_single_identical(
             test_max_difference=_UpperCAmelCase       ,							relax_max_difference=_UpperCAmelCase       ,							test_mean_pixel_difference=_UpperCAmelCase       ,							)





      @skip_mps
      def 							_lowercase					(		self    :       Optional[Any]							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  torch_device == """cpu"""
         UpperCAmelCase      					=  False

         self._test_attention_slicing_forward_pass(
             test_max_difference=_UpperCAmelCase       ,							test_mean_pixel_difference=_UpperCAmelCase       ,							)








'''simple docstring'''

import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import (
			    FocalNetBackbone,
			    FocalNetForImageClassification,
			    FocalNetForMaskedImageModeling,
			    FocalNetModel,
			)
			from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class 							a__:






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase=13					,   _UpperCAmelCase=32					,   _UpperCAmelCase=2					,   _UpperCAmelCase=3					,   _UpperCAmelCase=16					,   _UpperCAmelCase=[32, 64, 128]					,   _UpperCAmelCase=[1, 2, 1]					,   _UpperCAmelCase=[2, 2, 4]					,   _UpperCAmelCase=2					,   _UpperCAmelCase=2.0					,   _UpperCAmelCase=True					,   _UpperCAmelCase=0.0					,   _UpperCAmelCase=0.0					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase="gelu"					,   _UpperCAmelCase=False					,   _UpperCAmelCase=True					,   _UpperCAmelCase=0.02					,   _UpperCAmelCase=1E-5					,   _UpperCAmelCase=True					,   _UpperCAmelCase=None					,   _UpperCAmelCase=True					,   _UpperCAmelCase=10					,   _UpperCAmelCase=8					,   _UpperCAmelCase=["stage1", "stage2"]					,   _UpperCAmelCase=[1, 2]					,   )							->							int:
					snake_case__	     =parent
					snake_case__	     =batch_size
					snake_case__	     =image_size
					snake_case__	     =patch_size
					snake_case__	     =num_channels
					snake_case__	     =embed_dim
					snake_case__	     =hidden_sizes
					snake_case__	     =depths
					snake_case__	     =num_heads
					snake_case__	     =window_size
					snake_case__	     =mlp_ratio
					snake_case__	     =qkv_bias
					snake_case__	     =hidden_dropout_prob
					snake_case__	     =attention_probs_dropout_prob
					snake_case__	     =drop_path_rate
					snake_case__	     =hidden_act
					snake_case__	     =use_absolute_embeddings
					snake_case__	     =patch_norm
					snake_case__	     =layer_norm_eps
					snake_case__	     =initializer_range
					snake_case__	     =is_training
					snake_case__	     =scope
					snake_case__	     =use_labels
					snake_case__	     =type_sequence_label_size
					snake_case__	     =encoder_stride
					snake_case__	     =out_features
					snake_case__	     =out_indices






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

					snake_case__	     =None
					if self.use_labels:
							snake_case__	     =ids_tensor([self.batch_size]					,   self.type_sequence_label_size						)

					snake_case__	     =self.get_config()

					return config, pixel_values, labels






			def 						_lowercase  (     self						)							->							Any:
					return FocalNetConfig(
					    image_size=self.image_size					,   patch_size=self.patch_size					,   num_channels=self.num_channels					,   embed_dim=self.embed_dim					,   hidden_sizes=self.hidden_sizes					,   depths=self.depths					,   num_heads=self.num_heads					,   window_size=self.window_size					,   mlp_ratio=self.mlp_ratio					,   qkv_bias=self.qkv_bias					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   drop_path_rate=self.drop_path_rate					,   hidden_act=self.hidden_act					,   use_absolute_embeddings=self.use_absolute_embeddings					,   path_norm=self.patch_norm					,   layer_norm_eps=self.layer_norm_eps					,   initializer_range=self.initializer_range					,   encoder_stride=self.encoder_stride					,   out_features=self.out_features					,   out_indices=self.out_indices					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Union[str, Any]:
					snake_case__	     =FocalNetModel(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase						)

					snake_case__	     =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
					snake_case__	     =int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

					self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, expected_seq_len, expected_dim)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Optional[int]:
					snake_case__	     =FocalNetBackbone(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase						)

					# verify feature maps
					self.parent.assertEqual(len(result.feature_maps						)					,   len(config.out_features						)						)
					self.parent.assertListEqual(list(result.feature_maps[0].shape						)					,   [self.batch_size, self.image_size, 8, 8]						)

					# verify channels
					self.parent.assertEqual(len(model.channels						)					,   len(config.out_features						)						)
					self.parent.assertListEqual(model.channels					,   config.hidden_sizes[:-1]						)

					# verify backbone works with out_features=None
					snake_case__	     =None
					snake_case__	     =FocalNetBackbone(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase						)

					# verify feature maps
					self.parent.assertEqual(len(result.feature_maps						)					,   1						)
					self.parent.assertListEqual(list(result.feature_maps[0].shape						)					,   [self.batch_size, self.image_size * 2, 4, 4]						)

					# verify channels
					self.parent.assertEqual(len(model.channels						)					,   1						)
					self.parent.assertListEqual(model.channels					,   [config.hidden_sizes[-1]]						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =FocalNetForMaskedImageModeling(config=_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase						)
					self.parent.assertEqual(
					    result.reconstruction.shape					,   (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

					# test greyscale images
					snake_case__	     =1
					snake_case__	     =FocalNetForMaskedImageModeling(_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()

					snake_case__	     =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					snake_case__	     =model(_UpperCAmelCase						)
					self.parent.assertEqual(result.reconstruction.shape					,   (self.batch_size, 1, self.image_size, self.image_size)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Tuple:
					snake_case__	     =self.type_sequence_label_size
					snake_case__	     =FocalNetForImageClassification(_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()
					snake_case__	     =model(_UpperCAmelCase					,   labels=_UpperCAmelCase						)
					self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.type_sequence_label_size)						)

					# test greyscale images
					snake_case__	     =1
					snake_case__	     =FocalNetForImageClassification(_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()

					snake_case__	     =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					snake_case__	     =model(_UpperCAmelCase						)
					self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.type_sequence_label_size)						)






			def 						_lowercase  (     self						)							->							List[str]:
					snake_case__	     =self.prepare_config_and_inputs()

					snake_case__     ,	snake_case__     ,	snake_case__	     =config_and_inputs
					snake_case__	     ={'pixel_values': pixel_values}
					return config, inputs_dict





@require_torch
class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: str									=      (
			    (
			        FocalNetModel,
			        FocalNetForImageClassification,
			        FocalNetForMaskedImageModeling,
			        FocalNetBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			a_	: Union[str, Any]									=      (
			    {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
			    if is_torch_available()
			    else {}
			)
			a_	: Dict									=      False

			a_	: Any									=      False
			a_	: List[str]									=      False
			a_	: List[Any]									=      False
			a_	: Optional[Any]									=      False






			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__	     =FocalNetModelTester(self						)
					snake_case__	     =ConfigTester(self					,   config_class=_UpperCAmelCase					,   embed_dim=37					,   has_text_modality=_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					self.create_and_test_config_common_properties()
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()






			def 						_lowercase  (     self						)							->							str:
					return






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)






			@unittest.skip(reason='FocalNet does not use inputs_embeds'						)
			def 						_lowercase  (     self						)							->							str:
					pass






			@unittest.skip(reason='FocalNet does not use feedforward chunking'						)
			def 						_lowercase  (     self						)							->							int:
					pass






			def 						_lowercase  (     self						)							->							List[str]:
					snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes[:-1]:
							snake_case__	     =model_class(_UpperCAmelCase						)
							self.assertIsInstance(model.get_input_embeddings()					,   (nn.Module)						)
							snake_case__	     =model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(_UpperCAmelCase					,   nn.Linear						)						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes[:-1]:
							snake_case__	     =model_class(_UpperCAmelCase						)
							snake_case__	     =inspect.signature(model.forward						)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case__	     =[*signature.parameters.keys()]

							snake_case__	     =['pixel_values']
							self.assertListEqual(arg_names[:1]					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Union[str, Any]:
					snake_case__	     =model_class(_UpperCAmelCase						)
					model.to(_UpperCAmelCase						)
					model.eval()

					with torch.no_grad():
							snake_case__	     =model(**self._prepare_for_class(_UpperCAmelCase					,   _UpperCAmelCase						)						)

					snake_case__	     =outputs.hidden_states

					snake_case__	     =getattr(
					    self.model_tester					,   'expected_num_hidden_layers'					,   len(self.model_tester.depths						) + 1						)
					self.assertEqual(len(_UpperCAmelCase						)					,   _UpperCAmelCase						)

					# FocalNet has a different seq_length
					snake_case__	     =(
					    config.patch_size
					    if isinstance(config.patch_size					,   collections.abc.Iterable						)
					    else (config.patch_size, config.patch_size)
					)

					snake_case__	     =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

					self.assertListEqual(
					    list(hidden_states[0].shape[-2:]						)					,   [num_patches, self.model_tester.embed_dim]					,   )

					snake_case__	     =outputs.reshaped_hidden_states
					self.assertEqual(len(_UpperCAmelCase						)					,   _UpperCAmelCase						)

					snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =reshaped_hidden_states[0].shape
					snake_case__	     =(
					    reshaped_hidden_states[0].view(_UpperCAmelCase					,   _UpperCAmelCase					,   height * width						).permute(0					,   2					,   1						)
					)
					self.assertListEqual(
					    list(reshaped_hidden_states.shape[-2:]						)					,   [num_patches, self.model_tester.embed_dim]					,   )






			def 						_lowercase  (     self						)							->							List[str]:
					snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs_for_common()

					snake_case__	     =(
					    self.model_tester.image_size
					    if isinstance(self.model_tester.image_size					,   collections.abc.Iterable						)
					    else (self.model_tester.image_size, self.model_tester.image_size)
					)

					for model_class in self.all_model_classes[:-1]:
							snake_case__	     =True
							self.check_hidden_states_output(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							snake_case__	     =True

							self.check_hidden_states_output(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							List[str]:
					snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs_for_common()
					snake_case__	     =3

					snake_case__	     =(
					    self.model_tester.image_size
					    if isinstance(self.model_tester.image_size					,   collections.abc.Iterable						)
					    else (self.model_tester.image_size, self.model_tester.image_size)
					)
					snake_case__	     =(
					    config.patch_size
					    if isinstance(config.patch_size					,   collections.abc.Iterable						)
					    else (config.patch_size, config.patch_size)
					)

					snake_case__	     =image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
					snake_case__	     =image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

					for model_class in self.all_model_classes[:-1]:
							snake_case__	     =True
							self.check_hidden_states_output(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   (padded_height, padded_width)						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							snake_case__	     =True
							self.check_hidden_states_output(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   (padded_height, padded_width)						)






			@slow
			def 						_lowercase  (     self						)							->							Optional[Any]:
					for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__	     =FocalNetModel.from_pretrained(_UpperCAmelCase						)
							self.assertIsNotNone(_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							int:
					snake_case__     ,	snake_case__	     =self.model_tester.prepare_config_and_inputs_for_common()

					snake_case__	     =_config_zero_init(_UpperCAmelCase						)
					for model_class in self.all_model_classes:
							snake_case__	     =model_class(config=_UpperCAmelCase						)
							for name, param in model.named_parameters():
									if "embeddings" not in name and param.requires_grad:
											self.assertIn(
											    ((param.data.mean() * 1E9).round() / 1E9).item()					,   [0.0, 1.0]					,   msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""					,   )





@require_vision
@require_torch
class 							a__( unittest.TestCase     ):






			@cached_property
			def 						_lowercase  (     self						)							->							Any:
					# TODO update organization
					return AutoImageProcessor.from_pretrained('microsoft/focalnet-tiny'						) if is_vision_available() else None






			@slow
			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny'						).to(_UpperCAmelCase						)
					snake_case__	     =self.default_image_processor

					snake_case__	     =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
					snake_case__	     =image_processor(images=_UpperCAmelCase					,   return_tensors='pt'						).to(_UpperCAmelCase						)

					# forward pass
					with torch.no_grad():
							snake_case__	     =model(**_UpperCAmelCase						)

					# verify the logits
					snake_case__	     =torch.Size((1, 1000)						)
					self.assertEqual(outputs.logits.shape					,   _UpperCAmelCase						)
					snake_case__	     =torch.tensor([0.2_166, -0.4_368, 0.2_191]						).to(_UpperCAmelCase						)
					self.assertTrue(torch.allclose(outputs.logits[0, :3]					,   _UpperCAmelCase					,   atol=1E-4						)						)
					self.assertTrue(outputs.logits.argmax(dim=-1						).item()					,   281						)





@require_torch
class 							a__( snake_case__   ,      unittest.TestCase     ):
			a_	: int									=      (FocalNetBackbone,) if is_torch_available() else ()
			a_	: Any									=      FocalNetConfig

			a_	: int									=      False






			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__	     =FocalNetModelTester(self						)




